For the lowest-energy conformers, reorganization energies were computed at the GFN1-xTB level (λ GFN1 ). Electronic descriptor values entering property-based ML models (see below) were also extracted from results of these calculations. These include frontier orbital energies and their gaps, Fermi levels, total energies and vertical energy differences. Final target λ DFT values were calculated at the B3LYP 42-44 level of theory using the FHI-AIMS code, including the TS dispersion correction . Electronic wave functions were expanded in an extended tier 1 basis set using "light" integration settings. Note that this level of theory is commonly employed for characterizing organic semiconductors, thus forming a good reference method for this study. ML models. All models presented herein use GPR, a probabilistic machine learning method that allows for the smooth interpolation of property values from data. Specifically, these models infer the underlying relationship between different molecular representations and λ, based on a training set D = {X, y}. Here, X is a matrix consisting of molecular representation vectors x (i) and y is a vector of target properties for the training molecules, with elements y (i) . Predictions for a set of unseen molecular representations X * can then be obtained as the predictive mean