We specifically generated three fine-tuning datasets of different size to test MCRT's predictive capacity on limited data availability scenarios, namely LE all, a dataset with 73,779 structures composed of CSP landscapes on all the molecules listed in Fig. , LE T2, the CSP landspace of T2 with 8,293 structures and LE T2A, the CSP landspace of T2A with 1,367 structures. • CH 4 deliverable capacity. A dataset of 5,687 T2-based structures with calculated CH 4 deliverable capacity (298 K, 65-5.8 bar) was directly retrieved from the previous work . • CH 4 diffusivity. A dataset of 5,687 T2-based structures with calculated CH 4 diffusion coefficients at infinite dilution using the MD simulations. The simulations were conducted at 298 K with a time step of 1 fs for a total of 5 million cycles, with 1,000 cycles used for the initialization and 10,000 cycles for equilibration. DREIDING force field was used with the Lorentz-Berthelot mixing rule and a cut-off distance of 13 Å. The CH 4 molecule was modeled as a single atom. Prior to the simulations, 30 CH 4 molecules were randomly introduced into the pores of crystals. The mean square displacement (MSD) of gas molecules during 1-5 ns is used to calculate the diffusion coefficient through Einstein's relation . All these simulations were carried out at NVT ensemble using RASPA2 package . • Charge mobility. The charge carrier mobility values in this dataset were obtained from previous work and were calculated using the Marcus theory of charge transport. The dataset is based on crystal structure prediction (CSP) studies, with the studied molecules including pentacene and azapentacenes. The charge carrier mobility calculations were restricted to crystal structures within a 7 kJ/mol energy range of the global minimum on the energy-density landscapes, capturing low-energy polymorphs most likely to be observed experimentally. • ∆-E. The training target, ∆-E, represents the lattice energy difference between DFT (B86bPBE+XDM) and force field (FIT+DMA) accuracy. Following the approach in the original paper , which includes 1,000 CSP landscapes, we split the dataset by selecting 10 crystal structures from each of around 900 landscapes for training and validation, while about 100 landscapes, with 10 structures each, were reserved as a test set. An exclusion of duplicate structures was applied that led to a final dataset of 11,458 structures.