Historical texts are an important source to understand historical events and societal developments. In traditional literary studies, such documents are analyzed using close reading, which is tedious and time-consuming for large text corpora. Thus, computational literary scholars have adopted and developed automated and quantitative methods that complement and contribute to insights of close reading. In this case study, we show that text complexity measures are meaningful additions to these methods. Specifically, we apply interpretable measures of reading ease and of syntactic and lexical richness to historical texts and show that the obtained quantitative results are consistent with findings from close reading.