We present an open-source workflow for prediction of high-resolution EI mass spectra and performance assessment based on the QCxMS, matchms, RDKIT, xTB and GAMESS software packages. Additionally, we provide the largest set of predicted mass spectra of environmental chemicals so far and perform an unbiased analysis based on structural taxonomy classifications and chemical element composition. Our results show that further developments of SQM-based MD to improve prediction accuracy for molecules containing electronegative atoms (e.g., halogens, nitrogen) with high molecular flexibility (e.g., multiple rotatable bonds and stereo centers) are crucial. Current methods are insufficient for practical suspect screening applications which require at least 3 characteristic (i.e., highly abundant) ions for correct identification, as these conditions were not met for the vast majority of predicted spectra when compared with experimental high-resolution spectra. Furthermore, improvements with regard to computational efficiency and accessibility are essential to advance the field of QC based in silico mass spectra prediction. This especially holds true considering the need for additional comprehensive large scale studies which are required to characterize the applicable chemical space and potential pitfalls of these methodologies.