Grand Canonical Monte Carlo (GCMC) is a widely used method for simulating gas adsorption in nanoporous solids, including metal-organic frameworks (MOFs), zinc-imidazole frameworks (ZIFs), covalent organic frameworks (COFs), and zeolites. In these simulations the framework-adsorbate interactions are modeled using a classical force field. The van der Waals energy is calculated using the Lennard-Jones potential with specific parameters, while electrostatics are determined using partial atomic charges. It is crucial to select appropriate force field parameters and partial charge assignment schemes as they can significantly influence the simulation results. Recently we presented the CRAFTED database that contains approximately 50,000 simulated isotherms of CO2 and N2 on 690 MOF structures with a systematic selection of different force fields and temperatures. However the current version of the database does not include purely organic structures, such as covalent organic frameworks (COFs). Here we present an expansion of the CRAFTED database with simulated adsorption isotherms on 716 COF structures taken from the CURATED database. The simulations were performed for the adsorption of CO2 and N2 with two force fields obtained from all the possible combinations of Lennard-Jones parameters taken from two models (UFF and DREIDING) and six partial charge schemes (no charge, Qeq, EQeq, and DDEC, PACMOF, and MPNN). These simulations were performed at three temperatures (273, 298, and 323 K) and within pressure ranges of 0,001 to 1 bar for N2, and 0,001 to 10 bar for CO2. This new results introduces 51,552 adsorption isotherms on CRAFTED, which doubles its current size and provides a more comprehensive representation of the diversity of reticular materials chemistry. This expanded and more comprehensive dataset of adsorption isotherms enables a more detailed evaluation of the uncertainty introduced by the choice of force field in both molecular and process-level simulations. This, in turn, aids in the search for simulation schemes that have lower levels of uncertainty, allowing for the development of more accurate computational approaches for the search of new materials that can efficiently capture CO2.