To this end, we present a new dataset of hybrid DFTlevel reorganization energies for 10.900 carbon and hydrogen containing molecules consisting of up to sixty atoms and five rotatable bonds. A series of Gaussian Process Regression (GPR) models are developed for this dataset, both for straightforward structure/property mapping and ∆-ML 37 using a semiempirical baseline. We find that the conformational freedom of these molecules can introduce significant noise to this inference task, so that the performance of the models is strongly influenced by the conformer sampling method. We further show that significant improvements in the predictive performance are achieved by adopting the ∆-learning strategy. Finally, we critically evaluate the usefulness of the obtained ML methods for the discovery of low-λ structures in a diverse chemical space and for deducing molecular FIG. . Illustration of the adiabatic potential energy surfaces of neutral and cationic molecular states. The reorganization energy λ is here calculated from the four indicated points as λ = E0(R+) -E0(R0) + E+(R0) -E+(R+). Focusing on holes as charge carriers, E0 and E+ are the total energies of the neutral and cationic molecular states, evaluated at the equilibrium geometries R0 and R+ of the respective states. In practice, two equilibrium geometries thus need to be obtained. design rules.