Discourse-level translation remains a major challenge for machine translation (MT) systems, particularly for translation from Indian languages to English. This difficulty arises due to factors such as rich morphology, diverse syntactic structures, unmarked gender distinctions in pronouns, and the limited availability of discourse-aware training data. Existing evaluation benchmarks primarily focus on sentence-level translation quality and fail to capture important discourse phenomena such as pronoun resolution and lexical cohesion. To address this gap, we introduce IndicDISCO-MT, a parallel benchmark dataset covering translations from eight Indian languages such as Bengali, Gujarati, Hindi, Marathi, Kannada, Tamil, Telugu and Urdu to English. On top of this dataset, we are first to introduce DiscoAlign, a human annotated word to word alignment benchmark dataset, that captures correspondences between source and target words across languages. In addition, we propose two evaluation benchmarks, ProAlign and LexiAlign, designed to specifically assess the ability of large language models (LLMs) and MT systems to handle personal pronouns and lexical cohesion. Our evaluation of recent LLMs and MT systems on these benchmarks shows that although models achieve high overall translation quality, they still struggle to accurately preserve discourse-level phenomena. The proposed benchmarks provide a systematic framework for evaluating discourse aware translation and can facilitate the development of MT systems that generate more coherent and contextually consistent translations.