In this work, we use a multiscale modelling approach combining classical and quantum mechanical simulation tools with machine learning to microscopically investigate nanoscale morphology and electronic properties of PEDOT:PSS films. We start with the parameterization of a classical force field using density functional theory (DFT), followed by molecular dynamics (MD) simulations to generate simulation boxes of PEDOT:PSS with varying concentrations of D-sorbitol. The boxes are then analyzed using DFT, yielding the energy disorder of the PEDOT oligomers caused by their individual conformations in the amorphous film. However, charge transport mostly depends on the static disorder, averaging over fluctuations of each single molecule. The analysis of a dynamical time series (MD trajectory) of each molecule in the film is computationally too costly, so we employed the data obtained by analyzing the last MD timestep to train a neural network model that can predict the HOMO energies of arbitrary conformations of PEDOT oligomers at a fraction of the cost of a DFT calculation.